--- title: "VectorDBBench" type: "tool" slug: "zilliztech-vectordbbench" canonical_url: "https://www.graphcanon.com/tools/zilliztech-vectordbbench" github_url: "https://github.com/zilliztech/VectorDBBench" homepage_url: "https://zilliz.com/vector-database-benchmark-tool" stars: 1135 forks: 399 primary_language: "Python" license: "MIT" categories: ["data-retrieval", "vector-databases"] tags: ["vector-database", "benchmark", "vectordb", "cost-effectiveness", "performance", "vector-search"] updated_at: "2026-07-07T19:49:24.087909+00:00" --- # VectorDBBench > Benchmark for vector databases. VectorDBBench is a benchmark tool designed to compare the performance and cost-effectiveness of mainstream vector databases and cloud services, providing users with an intuitive interface to initiate benchmarks and view results. ## Facts - Repository: https://github.com/zilliztech/VectorDBBench - Homepage: https://zilliz.com/vector-database-benchmark-tool - Stars: 1,135 · Forks: 399 · Open issues: 146 · Watchers: 14 - Primary language: Python - License: MIT - Last pushed: 2026-07-06T02:44:07+00:00 ## Categories - [Data & Retrieval](/categories/data-retrieval.md) - [Vector Databases](/categories/vector-databases.md) ## Tags vector-database, benchmark, vectordb, cost-effectiveness, performance, vector-search ## Related tools - [transformers](/tools/huggingface-transformers.md) - 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models (★ 162,350) - [langflow](/tools/langflow-ai-langflow.md) - Langflow is a powerful platform for building and deploying AI-powered agents and workflows. 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(★ 58,449) ## README (excerpt) ```text # VectorDBBench(VDBBench): A Benchmark Tool for VectorDB ## What is VDBBench VDBBench is not just an offering of benchmark results for mainstream vector databases and cloud services, it's your go-to tool for the ultimate performance and cost-effectiveness comparison. Designed with ease-of-use in mind, VDBBench is devised to help users, even non-professionals, reproduce results or test new systems, making the hunt for the optimal choice amongst a plethora of cloud services and open-source vector databases a breeze. Understanding the importance of user experience, we provide an intuitive visual interface. This not only empowers users to initiate benchmarks at ease, but also to view comparative result reports, thereby reproducing benchmark results effortlessly. To add more relevance and practicality, we provide cost-effectiveness reports particularly for cloud services. This allows for a more realistic and applicable benchmarking process. Closely mimicking real-world production environments, we've set up diverse testing scenarios including insertion, searching, and filtered searching. To provide you with credible and reliable data, we've included public datasets from actual production scenarios, such as [SIFT](http://corpus-texmex.irisa.fr/), [GIST](http://corpus-texmex.irisa.fr/), [Cohere](https://huggingface.co/datasets/Cohere/wikipedia-22-12/tree/main/en), and a dataset generated by OpenAI from an opensource [raw dataset](https://huggingface.co/datasets/allenai/c4). It's fascinating to discover how a relatively unknown open-source database might excel in certain circumstances! Prepare to delve into the world of VDBBench, and let it guide you in uncovering your perfect vector database match. VDBBench is sponsored by Zilliz，the leading opensource vectorDB company behind Milvus. Choose smarter with VDBBench - start your free test on [zilliz cloud](https://zilliz.com/) today! **Leaderboard:** https://zilliz.com/benchmark ## 🎈 Announcement 🎈 **June 2026 update:** Full Text Search has landed in VectorDBBench. We now benchmark BM25-style retrieval across supported backends, starting with MS MARCO and HotpotQA datasets, payload profiles, recall, QPS, and load metrics ready to compare. See the [VectorDBBench Full Text Search Release Note](docs/release/2026-06-full-text-search.md) for the full rollout details and caveats. ## Quick Start ### Prerequirement ``` shell python >= 3.11 ``` ### Install **Install vectordb-bench with only PyMilvus** ```shell pip install vectordb-bench ``` **Install the specific database client** ```shell pip install 'vectordb-bench[pinecone]' ``` All the database client supported | Optional database client | install command | |--------------------------|---------------------------------------------| | pymilvus, zilliz_cloud (*default*) | `pip install vectordb-bench` | | qdrant | `pip install vectordb-bench[qdrant]` | | pinecone | `pip install vectordb-bench[pinecone]` | | weaviate | `pip install vectordb-bench[weaviate]` | | elastic, aliyun_elasticsearch| `pip install vectordb-bench[elastic]` | | pgvector, pgvectorscale, pgdiskann, alloydb, vectorchord | `pip install vectordb-bench[pgvector]` | | pgvecto.rs | `pip install vectordb-bench[pgvecto_rs]` | | redis | `pip install vectordb-bench[redis]` | | memorydb | `pip install vectordb-bench[memorydb]` | | chromadb | `pip install vectordb-bench[chromadb]` | | cockroachdb | `pip install vectordb-bench[cockroachdb]` | | awsopensearch | `pip install vectordb-bench[opensearch]` | | aliyun_opensearch | `pip install vectordb-bench[aliyun_opensearch]` | | mongodb | `pip install vectordb-bench[mongodb]` | | tidb | `pip install vectordb-bench[tidb]` | | vespa ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/zilliztech-vectordbbench`](/api/graphcanon/tools/zilliztech-vectordbbench) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_